In classical approaches to drug discovery, before the routine use of molecular biological methods, the activity of test and lead compounds were typically first analyzed by direct in vivo administration into animals to monitor a biological response, or alternatively, were tested in vitro using animal tissues. This drug discovery paradigm resulted in the identification of compounds with biological activity (efficacy) in the test animal but of unknown efficacy in humans. In some examples, efficacy in humans was weakly indicative of animal test systems that differ significantly from the human system. The result of such poorly predictive animal models was costly and time consuming.
In more recent paradigms of drug discovery, initial screening efforts are typically conducted in vitro on cloned human targets but additional secondary properties of lead candidates can then be complicated by lack of efficacy in in vivo animal models of choice. For example, the non-peptide substance P antagonist CP-96,345 showed high affinity (IC50=0.4 nM) for cloned human neurokinin-1 (NK-1) receptor, but only 40 nM IC50 at cloned rat NK1 receptor. Thus, compounds were much less efficacious in rat models than predicted. (Sachais et al. Journal of Biological Chemistry, Feb. 5, 1993 268(4):2319–2323; Fong et al. Journal of Biological Chemistry, Dec. 25, 1992 267(36):25668–25671.)
The problem of potent but highly species-specific compounds is being encountered with greater frequency as the use of cloned human receptors, enzymes, proteases, transporters and other gene products of interest to the drug discovery process, in high-throughput drug screening becomes standard procedure. An approach is needed that addresses this problem by (1) looking at in vitro predictors of in vivo efficacy in animals and (2) screening molecules potent at the human receptor for activity at various animal orthologues in order to identify dually active compounds, thus enabling one to predict, a priori, an animal model useful in early efficacy studies of potential drug candidates.